X-Factor Target Constrained Priority Mix Planning
Date Issued
2006
Date
2006
Author(s)
Chen, Ke-Ju
DOI
en-US
Abstract
The globalization of markets is forcing the semiconductor manufacturers to look for ways to improve their competitive advantages by focusing on supply chain management. The foundry fab is particularly critical in semiconductor supply chain because that represents large capital investments, usually in the range of US$1-1.5 Billion. Foundry fabs often have multiple priority levels of orders, and higher priority must be given to some urgent lots to be competitive and to satisfy customers’ demand of accelerating the speed of products entering into the market so a product mix with different multiple priority lots has different and great influence on the production system and poses a great challenge to wafer fabrication.
In order to plan priority mix, we need to know the performance of fab. To evaluate the performance of the current factory, we need to model fab performances. Among the many fab performance indices, cycle time has a significant impact on productivity learning and customer serviceability. To measure and manage cycle times, the notion of X-factor = cycle time/raw processing time has been introduced to provide a sensitive performance indicator, which is standardized across different products.
In this thesis, we consider the priority mix planning under X-Factor target constrains to obtain the best profit because that makes sure the best solution of planning problem above the specific quality of service. To mathematically formulate the priority mix planning problem, we need a fab Priority X-Factor (PXF) model to analyze the relationship between PXF, release rate and capacity utilization. In view of the modeling power of Queueing models in describing fab performances is only to give mean and variance of release rate and service time.
Our key ideas of modeling fab X-Factor are divided fab into three levels: overall fab, stages of process and individual priorities. For single stage, we adopt M/G/1: PR model as an approximation. It has a closed form to calculate for convenience but its disadvantages are ignoring the variance of release rate and cause calculation of the residual service time to lose accuracy for X-Factor we then propose ideas of modifying fab PXF model from M/G/1: PR to G/G/1: PR by adding variance term of release rate in residual service time. To calculate fab network X-Factor, we propose Priority X-Factor Contribution (PXFC) theory that describes the relationship of X-Factor between fab, stage and individual priorities. We can calculate fab X-Factor by summing stages X-Factor multiplied by relative processing time and calculate stage X-Factor by summing individual priorities’ X-Factor multiplied by relative utilization.
To obtain issues of PXF modeling for planning problem, we study some numerical experiments of PXF model to analyze the influence of release rate of each priority, numbers of priority and capacity utilization on XF. And try to compare our network PXF model to simulation results from eM-Plant under the same input data but verification is not our major goal.
We then incorporate the PXF model and issues of PXF numerical studies to formulate the planning problem. To consider the price of orders and cost in our problem formulation; for price, we calculate revenue by multiplying release rate and unit price of wafer; for cost, we ignore the fixed cost – machine cost because it is a constant and isn’t changed by release rate. We divide variable cost into inventory cost and manufacturing cost; inventory cost is controlled by WIP and manufacturing cost is calculated by multiplying release rate and unit cost of wafer. Finally, we make some numerical studies on our formulation to analyze the relation between profit, cost, revenue, and release rate of individual priorities and obtain insights of this research for managements.
Subjects
差別服務比例
X-因子
Priority Mix
X-Factor
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-95-R93921070-1.pdf
Size
23.31 KB
Format
Adobe PDF
Checksum
(MD5):bdc098a9df179a729796b1222ce6baf7